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Neural Computing and Applications

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26.03.2021 | Original Article

Transfer learning to detect neonatal seizure from electroencephalography signals

verfasst von: Abdullah Caliskan, Suleyman Rencuzogullari

Erschienen in: Neural Computing and Applications | Ausgabe 18/2021

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Abstract

This paper offers a solution to the problem of detecting neonatal seizures via a transfer learning technique that judiciously reconstructs pre-trained deep convolution neural networks (p-DCNN), including alexnet, resnet18, googlenet, densenet, and resnet50. Multichannel electroencephalography (EEG) signals are converted to colour images for feeding them as an input for the p-DCNN. A deep neural network (DNN) such as a convolution neural network (CNN) may be directly used instead of transfer learning-based networks. However, a DNN requires too much training data, too much training time, and a computer with high-performance computational capability. The DNN also has several user-supplied hyper-parameters that must be tuned to obtain desirable classification success. To prevent these drawbacks, we propose a transfer learning technique to solve the neonatal seizures detection problem. Results of simulations and the statistical analysis enable us to devise a transfer learning technique employed for seizure detection.

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Titel: Transfer learning to detect neonatal seizure from electroencephalography signals
verfasst von: Abdullah Caliskan
Suleyman Rencuzogullari
Publikationsdatum: 26.03.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 18/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05878-y

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